Abstract
We propose a novel probabilistic approach to learning spatial representations of dynamic environments from 3D laser range measurements. Whilst most of the previous techniques developed in robotics address this problem by computationally expensive tracking frameworks, our method performs in real-time even in the presence of large amounts of dynamic objects. The computer vision community has provided comparable methods for learning foreground activity patterns in images. However, these methods generally do not account well for the uncertainty involved in the sensing process. In this paper, we show that the problem of detecting occurrences of non-stationary objects in range readings can be solved online under the assumption of a consistent Bayesian framework. Whilst the model underlying our framework naturally scales with the complexity and the noise characteristics of the environment, all parameters involved in the detection process obey a clean probabilistic interpretation. When applied to real-world urban settings, the results produced by our approach appear promising and may directly be applied to solve map building, localization, or robot navigation problems.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Biber, P., Duckett, T.: Dynamic maps for long-term operation of mobile service robots. In: Proc. of Robotics: Science and Systems, RSS (2005)
Bishop, C., et al.: Pattern Recognition and Machine Learning, pp. 94–97. Springer, New York (2006)
Burgard, W., Stachniss, C., Hahnel, D.: Mobile robot map learning from range data in dynamic environments. STAR, vol. 35 (2007)
Hershey, J., Olsen, P.: Approximating the Kullback Leibler divergence between Gaussian mixture models. In: Proc. of The International Conference on Acoustics, Speech, and Signal Processing (ICASSP), vol. 4, pp. 317–320 (2007)
Hou, S., Galata, A.: Robust estimation of Gaussian mixtures from noisy input data. In: Proc. of The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–8 (2008)
Jensen, B., Philippsen, R., Siegwart, R.: Motion detection and path planning in dynamic environments. In: Workshop Proceedings Reasoning with Uncertainty in Robotics, International Joint Conference on Artificial Intelligence, IJCAI (2003)
Kaestner, R., Thrun, S., Montemerlo, M., Whalley, M.: A non-rigid approach to scan alignment and change detection using range sensor data. In: Field and Service Robotics. STAR, 25th edn., pp. 179–194. Springer (2006)
Lee, D., Hull, J., Erol, B.: A Bayesian framework for Gaussian mixture background modeling. In: Proc. of The IEEE International Conference on Image Processing, vol. 3, pp. 973–976 (2003)
Lerner, U.: Hybrid Bayesian Networks for Reasoning about Complex Systems. PhD thesis, Stanford University (2002)
Luber, M., Arras, K., Plagemann, C., Burgard, W.: Classifying dynamic objects: An unsupervised learning approach. In: Robotics: Science and Systems IV, p. 270 (2009)
Roy, N., Burgard, W., Fox, D., Thrun, S.: Coastal navigation: Mobile robot navigation with uncertainty in dynamic environments. In: IEEE International Conference on Robotics and Automation, pp. 35–40. Citeseer (1999)
Schulz, D., Burgard, W.: Probabilistic state estimation of dynamic objects with a moving mobile robot. Robotics and Autonomous Systems 34(2-3), 107–115 (2001)
Sheikh, Y., Shah, M.: Bayesian object detection in dynamic scenes. In: Proc. of The IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, p. 74 (2005)
Stauffer, C., Grimson, W.: Learning patterns of activity using real-time tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(8), 747–757 (2000)
Surmann, H., Nüchter, A., Hertzberg, J.: An autonomous mobile robot with a 3D laser range finder for 3D exploration and digitalization of indoor environments. Journal of Robotics and Autonomous Systems (JRAS) 45(3-4) (2003)
Triebel, R., Pfaff, P., Burgard, W.: Multi-level surface maps for outdoor terrain mapping and loop closing. In: Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, IROS (2006)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag GmbH Berlin Heidelberg
About this chapter
Cite this chapter
Kästner, R., Engelhard, N., Triebel, R., Siegwart, R. (2014). A Bayesian Approach to Learning 3D Representations of Dynamic Environments. In: Khatib, O., Kumar, V., Sukhatme, G. (eds) Experimental Robotics. Springer Tracts in Advanced Robotics, vol 79. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28572-1_32
Download citation
DOI: https://doi.org/10.1007/978-3-642-28572-1_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-28571-4
Online ISBN: 978-3-642-28572-1
eBook Packages: EngineeringEngineering (R0)